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This paper overviews the design and rationale of these two studies, in addition to a description of approaches for biomarker discovery focused on existing cohort studies that are accessible to the Consortium.
To enrich for markers destined to be found in the peripheral circulation of lung cancer patients, discovery focused on glycoproteins predicted to be located either at the cell membrane or secreted/shed from lung cancer cells.
Of necessity with so few markers, QTL discovery focused on a limited genetic base represented by F1 progenies of a bi-parental population, which, along with the expected absence of individual markers with high yield effect, limited the practicality of a marker-assisted selection program.
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Much of today's drug discovery focuses on biologically sourced drugs as opposed to chemically synthesized ones.
Ordered mesoporous carbons (OMCs) have been studied extensively since their discovery focusing on their synthesis control as well as their applications such as catalyst support [1], electrodes materials [2], and delivering processing [3].
The process of resolving many requirements is termed 'multi-objective optimization', and here we discuss how this can be used for drug discovery, focusing on evolutionary molecule design to incorporate optimal predicted absorption, distribution, metabolism, excretion and toxicity properties.
Furthermore, these E-dependent expression datasets will enable further gene discovery focusing on microRNAs and non-coding RNAs.
Motif discovery focuses on searching for novel binding motifs from a collection of short sequences that are assumed to contain a common regulatory motif.
Kornbluth and Stone have recently hailed a new golden age of vaccine discovery focusing on the exploitation of adjuvants as immunomodulators able to enhance immunogencity of subunit and peptide-based vaccines [38].
In this study we approach the broad question of applying computation to deep experimental expression datasets to develop methods for gene discovery, focusing on miRNAs, ncRNAs, and antisense transcripts.
Another major direction of motif finding and CRN discovery focuses on comparative genomics, also referred to as phylogenetic footprinting (PF) [ 22- 34].
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